Currently, an increase in system scale and a phenomenon where a field that has not been systematized is suddenly turned into a large-scale system are in progress in various fields. A typical example of the former is found in the IT field such as data centers and networks that are related to a technology called cloud, and the scale thereof is increasing dramatically year after year. An example of the latter is found in the infrastructure field such as electric power, energy networks, and cities, which are becoming targets to be controlled as fields of a smart grid, a smart city, or other large-scale system cases. How large-scale systems are to be controlled is therefore expected to be a very important issue in the near future.
Add to that, the energy-conscious mindset of today's society demands control that does not waste energy by shutting down an idling machine or the like. This is a trend not exclusive to the IT field and the infrastructure field, but shared throughout the society. A generally effective action is, for example, exerting control so as to shut down a control object that is idling.
In the case of electric power grids, though limited electric power plants are controlled in this manner at present, an issue of which power plant is to be activated and which power plant is to be shut down for efficient generation of electricity has been studied for a long time. This is a matter of load balancing and is called a “unit commitment problem”. The “unit commitment problem” includes not only the issue of which power plants is to be activated/shut down but also an issue of allocating load optimally among active power plants after determining for each power plant whether to activate or shut down, and an issue of adjusting load in active power plants. This is a comprehensive matter of activation/shutdown and optimal allocation of generation amount and load. A solution method that has been employed is offline scheduling based on a scenario that is scripted as a deterministic mathematical programming model.
If the society steers toward further energy saving, innumerable solar energy sources are expected to be connected to conventional power grids. With solar light energy which fluctuates depending on the goings-on in nature, the conventional method of running power plants by offline scheduling would not work well. As is understood from this prediction, solving the problem means being capable of dealing with a very large number of control objects and dealing with an unexpected external disturbance. Offline scheduling is not good at dealing with an unexpected external disturbance, and a method of solving the “unit commitment problem” in real time for innumerable electric power nodes (on a large scale) will be sought after in the future.
However, a method of solving the “unit commitment problem” in real time is very difficult to think up of and has not been proposed at present. There is also an issue of how to handle a very large number of control objects.
The “unit commitment problem” exists in the IT field as well.
Examples of control exerted in a system of the IT field include load balancing among computer resources, network load balancing, and decentralized storage arrangement, namely, a decentralized control of function blocks. How load balancing among computer resources is controlled is described by taking as an example a load balancing method that is targeted for a data center (DC) including a plurality of servers.
The basic idea of conventional control eventuates in “balancing the internal state of the system”, which is rather unsophisticated. With this mindset, while the control policy is clear in the case of a uniform system (where the plurality of servers are identical servers), what index is to be the basis of balancing is not clear in a mixed-machine system where different types of machines are used.
For instance, while it is known that the CPU utilization ratios of the respective servers are to be balanced in the case of servers having the same performance, it is not obvious how to balance the CPU utilization ratios of servers that have different performance characteristics in a manner that reflects the servers' respective performance. A result obtained by the queueing theory, which is an index often used in conventional control, is an index based on the theory of probability, and tells nothing about what means to use in order to lead the system to a stochastically steady state.
What this means is that, although the system may eventually satisfy requirements to be fulfilled such as response and throughput by a control that uses the queueing theory, the system has no guarantees or limitations with regard to processes in the middle and the situation of resources that are ultimately used. In other words, the resultant control is merely for fulfilling the required performance irrespective of whether the system is in a very inefficient state in terms of energy.
Under such control, the inefficiency grows as the system increases in scale, and is expected to be a serious problem. Shutting down a server that is to be shut down and activating a server that is to be activated with accuracy is important here, too, and server load balancing can be considered as a type of the “unit commitment problem”.
The “unit commitment problem” is a problem also associated with many control problems such as storage arrangement control.
Examples of technologies related to the system control described above are found in the following patent literatures.